{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table style=\"float:left; border:none\">\n",
    "   <tr style=\"border:none; background-color: #ffffff\">\n",
    "       <td style=\"border:none\">\n",
    "           <a href=\"http://bokeh.org/\">     \n",
    "           <img \n",
    "               src=\"assets/bokeh-transparent.png\" \n",
    "               style=\"width:50px\"\n",
    "           >\n",
    "           </a>    \n",
    "       </td>\n",
    "       <td style=\"border:none\">\n",
    "           <h1>Bokeh Tutorial</h1>\n",
    "       </td>\n",
    "   </tr>\n",
    "</table>\n",
    "\n",
    "<div style=\"float:right;\"><h2>07. Bar and Categorical Data Plots</h2></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"1001\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id != null && id in Bokeh.index) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var id = msg.content.text.trim();\n",
       "            if (id in Bokeh.index) {\n",
       "              Bokeh.index[id].model.document.clear();\n",
       "              delete Bokeh.index[id];\n",
       "            }\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"1001\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
       "        if (callback != null)\n",
       "          callback();\n",
       "      });\n",
       "    } finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(css_urls, js_urls, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
       "\n",
       "    function on_load() {\n",
       "      root._bokeh_is_loading--;\n",
       "      if (root._bokeh_is_loading === 0) {\n",
       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
       "        run_callbacks()\n",
       "      }\n",
       "    }\n",
       "\n",
       "    function on_error() {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < css_urls.length; i++) {\n",
       "      var url = css_urls[i];\n",
       "      const element = document.createElement(\"link\");\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.rel = \"stylesheet\";\n",
       "      element.type = \"text/css\";\n",
       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
       "      document.body.appendChild(element);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "  };var element = document.getElementById(\"1001\");\n",
       "  if (element == null) {\n",
       "    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  function inject_raw_css(css) {\n",
       "    const element = document.createElement(\"style\");\n",
       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
       "  }\n",
       "\n",
       "  \n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n",
       "  var css_urls = [];\n",
       "  \n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "    \n",
       "    \n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if (root.Bokeh !== undefined || force === true) {\n",
       "      \n",
       "    for (var i = 0; i < inline_js.length; i++) {\n",
       "      inline_js[i].call(root, root.Bokeh);\n",
       "    }\n",
       "    if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"1001\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };var element = document.getElementById(\"1001\");\n  if (element == null) {\n    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n    return false;\n  }\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  \n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n  var css_urls = [];\n  \n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {\n    \n    \n    }\n  ];\n\n  function run_inline_js() {\n    \n    if (root.Bokeh !== undefined || force === true) {\n      \n    for (var i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n    if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.io import show, output_notebook\n",
    "from bokeh.plotting import figure\n",
    "\n",
    "output_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Bar Charts\n",
    "\n",
    "Bar charts are a common and important type of plot. Bokeh makes it simple to create all sorts of stacked or nested bar charts, and to deal with categorical data in general."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The example below shows a simple bar chart created using the `vbar` method for drawing vertical bars. (There is a corresponding `hbar` for horizontal bars.) We also set a few plot properties to make the chart look nicer, see chapter [Styling and Theming](02 - Styling and Theming.ipynb) for information about visual properties."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"b36aa08e-b401-4e08-b974-36c6e5bf657f\" data-root-id=\"1002\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = {\"e8cb0f29-b149-42a3-8498-604d0ee064b3\":{\"roots\":{\"references\":[{\"attributes\":{\"below\":[{\"id\":\"1013\",\"type\":\"CategoricalAxis\"}],\"center\":[{\"id\":\"1016\",\"type\":\"Grid\"},{\"id\":\"1021\",\"type\":\"Grid\"}],\"left\":[{\"id\":\"1017\",\"type\":\"LinearAxis\"}],\"plot_height\":250,\"renderers\":[{\"id\":\"1038\",\"type\":\"GlyphRenderer\"}],\"title\":{\"id\":\"1003\",\"type\":\"Title\"},\"toolbar\":{\"id\":\"1028\",\"type\":\"Toolbar\"},\"x_range\":{\"id\":\"1005\",\"type\":\"FactorRange\"},\"x_scale\":{\"id\":\"1009\",\"type\":\"CategoricalScale\"},\"y_range\":{\"id\":\"1007\",\"type\":\"DataRange1d\"},\"y_scale\":{\"id\":\"1011\",\"type\":\"LinearScale\"}},\"id\":\"1002\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{},\"id\":\"1011\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"1027\",\"type\":\"HelpTool\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"1022\",\"type\":\"PanTool\"},{\"id\":\"1023\",\"type\":\"WheelZoomTool\"},{\"id\":\"1024\",\"type\":\"BoxZoomTool\"},{\"id\":\"1025\",\"type\":\"SaveTool\"},{\"id\":\"1026\",\"type\":\"ResetTool\"},{\"id\":\"1027\",\"type\":\"HelpTool\"}]},\"id\":\"1028\",\"type\":\"Toolbar\"},{\"attributes\":{\"formatter\":{\"id\":\"1042\",\"type\":\"CategoricalTickFormatter\"},\"ticker\":{\"id\":\"1014\",\"type\":\"CategoricalTicker\"}},\"id\":\"1013\",\"type\":\"CategoricalAxis\"},{\"attributes\":{},\"id\":\"1018\",\"type\":\"BasicTicker\"},{\"attributes\":{},\"id\":\"1014\",\"type\":\"CategoricalTicker\"},{\"attributes\":{\"callback\":null,\"factors\":[\"Apples\",\"Pears\",\"Nectarines\",\"Plums\",\"Grapes\",\"Strawberries\"]},\"id\":\"1005\",\"type\":\"FactorRange\"},{\"attributes\":{\"grid_line_color\":null,\"ticker\":{\"id\":\"1014\",\"type\":\"CategoricalTicker\"}},\"id\":\"1016\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"1047\",\"type\":\"Selection\"},{\"attributes\":{\"dimension\":1,\"ticker\":{\"id\":\"1018\",\"type\":\"BasicTicker\"}},\"id\":\"1021\",\"type\":\"Grid\"},{\"attributes\":{\"formatter\":{\"id\":\"1044\",\"type\":\"BasicTickFormatter\"},\"ticker\":{\"id\":\"1018\",\"type\":\"BasicTicker\"}},\"id\":\"1017\",\"type\":\"LinearAxis\"},{\"attributes\":{\"fill_color\":{\"value\":\"#1f77b4\"},\"line_color\":{\"value\":\"#1f77b4\"},\"top\":{\"field\":\"top\"},\"width\":{\"value\":0.9},\"x\":{\"field\":\"x\"}},\"id\":\"1036\",\"type\":\"VBar\"},{\"attributes\":{},\"id\":\"1044\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"1042\",\"type\":\"CategoricalTickFormatter\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"top\":{\"field\":\"top\"},\"width\":{\"value\":0.9},\"x\":{\"field\":\"x\"}},\"id\":\"1037\",\"type\":\"VBar\"},{\"attributes\":{\"data_source\":{\"id\":\"1035\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1036\",\"type\":\"VBar\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1037\",\"type\":\"VBar\"},\"selection_glyph\":null,\"view\":{\"id\":\"1039\",\"type\":\"CDSView\"}},\"id\":\"1038\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"1022\",\"type\":\"PanTool\"},{\"attributes\":{\"source\":{\"id\":\"1035\",\"type\":\"ColumnDataSource\"}},\"id\":\"1039\",\"type\":\"CDSView\"},{\"attributes\":{\"text\":\"Fruit Counts\"},\"id\":\"1003\",\"type\":\"Title\"},{\"attributes\":{},\"id\":\"1023\",\"type\":\"WheelZoomTool\"},{\"attributes\":{},\"id\":\"1009\",\"type\":\"CategoricalScale\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"1045\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"overlay\":{\"id\":\"1045\",\"type\":\"BoxAnnotation\"}},\"id\":\"1024\",\"type\":\"BoxZoomTool\"},{\"attributes\":{\"callback\":null,\"data\":{\"top\":[5,3,4,2,4,6],\"x\":[\"Apples\",\"Pears\",\"Nectarines\",\"Plums\",\"Grapes\",\"Strawberries\"]},\"selected\":{\"id\":\"1047\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"1046\",\"type\":\"UnionRenderers\"}},\"id\":\"1035\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"1046\",\"type\":\"UnionRenderers\"},{\"attributes\":{},\"id\":\"1025\",\"type\":\"SaveTool\"},{\"attributes\":{},\"id\":\"1026\",\"type\":\"ResetTool\"},{\"attributes\":{\"callback\":null,\"start\":0},\"id\":\"1007\",\"type\":\"DataRange1d\"}],\"root_ids\":[\"1002\"]},\"title\":\"Bokeh Application\",\"version\":\"1.4.0\"}};\n",
       "  var render_items = [{\"docid\":\"e8cb0f29-b149-42a3-8498-604d0ee064b3\",\"roots\":{\"1002\":\"b36aa08e-b401-4e08-b974-36c6e5bf657f\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
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      "application/vnd.bokehjs_exec.v0+json": ""
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      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1002"
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     "output_type": "display_data"
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   "source": [
    "# Here is a list of categorical values (or factors)\n",
    "fruits = ['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries']\n",
    "\n",
    "# Set the x_range to the list of categories above\n",
    "p = figure(x_range=fruits, plot_height=250, title=\"Fruit Counts\")\n",
    "\n",
    "# Categorical values can also be used as coordinates\n",
    "p.vbar(x=fruits, top=[5, 3, 4, 2, 4, 6], width=0.9)\n",
    "\n",
    "# Set some properties to make the plot look better\n",
    "p.xgrid.grid_line_color = None\n",
    "p.y_range.start = 0\n",
    "\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we want to create a plot with a categorical range, we pass the ordered list of categorical values to `figure`, e.g. `x_range=['a', 'b', 'c']`. In the plot above, we passed the list of fruits as `x_range`, and we can see those refelected as the x-axis.\n",
    "\n",
    "The `vbar` glyph method takes an `x` location for the center of the bar, a `top` and `bottom` (which defaults to 0), and a `width`. When we are using a categorical range as we are here, each category implicitly has width of 1, so setting `width=0.9` as we have done here makes the bars shrink away from each other. (Another option would be to add some padding to the range.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exercise: Create your own simple bar chart\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since `vbar` is a glyph method, we can use it with a `ColumnDataSource` just as we woudl with any other glyph. In the example below, we put the data (including color data) in a `ColumnDataSource` and use that to drive our plot. We also add a legend, see chapter [Adding Annotations.ipynb](03 - Adding Annotations.ipynb) for more information about legends and other annotations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"2f4afc72-61f1-4437-9cf0-e1cad0ec8f60\" data-root-id=\"1208\"></div>\n"
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       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = {\"34147b48-9269-4094-96f4-fc6a4e8bdfeb\":{\"roots\":{\"references\":[{\"attributes\":{\"below\":[{\"id\":\"1219\",\"type\":\"CategoricalAxis\"}],\"center\":[{\"id\":\"1222\",\"type\":\"Grid\"},{\"id\":\"1227\",\"type\":\"Grid\"},{\"id\":\"1252\",\"type\":\"Legend\"}],\"left\":[{\"id\":\"1223\",\"type\":\"LinearAxis\"}],\"plot_height\":250,\"renderers\":[{\"id\":\"1244\",\"type\":\"GlyphRenderer\"}],\"title\":{\"id\":\"1209\",\"type\":\"Title\"},\"toolbar\":{\"id\":\"1234\",\"type\":\"Toolbar\"},\"x_range\":{\"id\":\"1211\",\"type\":\"FactorRange\"},\"x_scale\":{\"id\":\"1215\",\"type\":\"CategoricalScale\"},\"y_range\":{\"id\":\"1213\",\"type\":\"Range1d\"},\"y_scale\":{\"id\":\"1217\",\"type\":\"LinearScale\"}},\"id\":\"1208\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{},\"id\":\"1229\",\"type\":\"WheelZoomTool\"},{\"attributes\":{},\"id\":\"1215\",\"type\":\"CategoricalScale\"},{\"attributes\":{\"overlay\":{\"id\":\"1251\",\"type\":\"BoxAnnotation\"}},\"id\":\"1230\",\"type\":\"BoxZoomTool\"},{\"attributes\":{\"callback\":null,\"factors\":[\"Apples\",\"Pears\",\"Nectarines\",\"Plums\",\"Grapes\",\"Strawberries\"]},\"id\":\"1211\",\"type\":\"FactorRange\"},{\"attributes\":{},\"id\":\"1231\",\"type\":\"SaveTool\"},{\"attributes\":{\"text\":\"Fruit Counts\"},\"id\":\"1209\",\"type\":\"Title\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"top\":{\"field\":\"counts\"},\"width\":{\"value\":0.9},\"x\":{\"field\":\"fruits\"}},\"id\":\"1243\",\"type\":\"VBar\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"1251\",\"type\":\"BoxAnnotation\"},{\"attributes\":{},\"id\":\"1232\",\"type\":\"ResetTool\"},{\"attributes\":{},\"id\":\"1217\",\"type\":\"LinearScale\"},{\"attributes\":{\"formatter\":{\"id\":\"1248\",\"type\":\"CategoricalTickFormatter\"},\"ticker\":{\"id\":\"1220\",\"type\":\"CategoricalTicker\"}},\"id\":\"1219\",\"type\":\"CategoricalAxis\"},{\"attributes\":{\"callback\":null,\"data\":{\"color\":[\"#3288bd\",\"#99d594\",\"#e6f598\",\"#fee08b\",\"#fc8d59\",\"#d53e4f\"],\"counts\":[5,3,4,2,4,6],\"fruits\":[\"Apples\",\"Pears\",\"Nectarines\",\"Plums\",\"Grapes\",\"Strawberries\"]},\"selected\":{\"id\":\"1277\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"1276\",\"type\":\"UnionRenderers\"}},\"id\":\"1207\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"1233\",\"type\":\"HelpTool\"},{\"attributes\":{},\"id\":\"1224\",\"type\":\"BasicTicker\"},{\"attributes\":{\"label\":{\"field\":\"fruits\"},\"renderers\":[{\"id\":\"1244\",\"type\":\"GlyphRenderer\"}]},\"id\":\"1253\",\"type\":\"LegendItem\"},{\"attributes\":{},\"id\":\"1220\",\"type\":\"CategoricalTicker\"},{\"attributes\":{},\"id\":\"1276\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"grid_line_color\":null,\"ticker\":{\"id\":\"1220\",\"type\":\"CategoricalTicker\"}},\"id\":\"1222\",\"type\":\"Grid\"},{\"attributes\":{\"items\":[{\"id\":\"1253\",\"type\":\"LegendItem\"}],\"location\":\"top_center\",\"orientation\":\"horizontal\"},\"id\":\"1252\",\"type\":\"Legend\"},{\"attributes\":{},\"id\":\"1248\",\"type\":\"CategoricalTickFormatter\"},{\"attributes\":{\"dimension\":1,\"ticker\":{\"id\":\"1224\",\"type\":\"BasicTicker\"}},\"id\":\"1227\",\"type\":\"Grid\"},{\"attributes\":{\"formatter\":{\"id\":\"1250\",\"type\":\"BasicTickFormatter\"},\"ticker\":{\"id\":\"1224\",\"type\":\"BasicTicker\"}},\"id\":\"1223\",\"type\":\"LinearAxis\"},{\"attributes\":{\"source\":{\"id\":\"1207\",\"type\":\"ColumnDataSource\"}},\"id\":\"1245\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"1250\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"fill_color\":{\"field\":\"color\"},\"line_color\":{\"field\":\"color\"},\"top\":{\"field\":\"counts\"},\"width\":{\"value\":0.9},\"x\":{\"field\":\"fruits\"}},\"id\":\"1242\",\"type\":\"VBar\"},{\"attributes\":{},\"id\":\"1277\",\"type\":\"Selection\"},{\"attributes\":{\"callback\":null,\"end\":9},\"id\":\"1213\",\"type\":\"Range1d\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"1228\",\"type\":\"PanTool\"},{\"id\":\"1229\",\"type\":\"WheelZoomTool\"},{\"id\":\"1230\",\"type\":\"BoxZoomTool\"},{\"id\":\"1231\",\"type\":\"SaveTool\"},{\"id\":\"1232\",\"type\":\"ResetTool\"},{\"id\":\"1233\",\"type\":\"HelpTool\"}]},\"id\":\"1234\",\"type\":\"Toolbar\"},{\"attributes\":{\"data_source\":{\"id\":\"1207\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1242\",\"type\":\"VBar\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1243\",\"type\":\"VBar\"},\"selection_glyph\":null,\"view\":{\"id\":\"1245\",\"type\":\"CDSView\"}},\"id\":\"1244\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"1228\",\"type\":\"PanTool\"}],\"root_ids\":[\"1208\"]},\"title\":\"Bokeh Application\",\"version\":\"1.4.0\"}};\n",
       "  var render_items = [{\"docid\":\"34147b48-9269-4094-96f4-fc6a4e8bdfeb\",\"roots\":{\"1208\":\"2f4afc72-61f1-4437-9cf0-e1cad0ec8f60\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
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       "id": "1208"
      }
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     "output_type": "display_data"
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   ],
   "source": [
    "from bokeh.models import ColumnDataSource\n",
    "from bokeh.palettes import Spectral6\n",
    "\n",
    "fruits = ['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries']\n",
    "counts = [5, 3, 4, 2, 4, 6]\n",
    "\n",
    "source = ColumnDataSource(data=dict(fruits=fruits, counts=counts, color=Spectral6))\n",
    "\n",
    "p = figure(x_range=fruits, plot_height=250, y_range=(0, 9), title=\"Fruit Counts\")\n",
    "p.vbar(x='fruits', top='counts', width=0.9, color='color', legend_field=\"fruits\", source=source)\n",
    "\n",
    "p.xgrid.grid_line_color = None\n",
    "p.legend.orientation = \"horizontal\"\n",
    "p.legend.location = \"top_center\"\n",
    "\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exercise: Create your own simple bar chart driven by a ColumnDataSource\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stacked Bars\n",
    "\n",
    "It's often desirable to stack bars together. Bokeh makes this straightforward using the `vbar_stack` and `hbar_stack` methods. When passing data to one of these methods, the data source should have a series for each \"row\" in the stack. You will provide an ordered list of column names to stack together from the data source. \n",
    "\n",
    "In the example below, we see simulated data for fruit exports (positive values) and imports (negative values) stacked using two calls to `hbar_stack`. The values in the columns for each year are ordered according to the `fruits`, i.e. this is not a \"tidy\" data format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"a4c40988-b3c5-486a-80f8-5eaf335e4d76\" data-root-id=\"1554\"></div>\n"
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      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = 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Application\",\"version\":\"1.4.0\"}};\n",
       "  var render_items = [{\"docid\":\"205f051c-5ca7-4b07-84ce-2785e663e02a\",\"roots\":{\"1554\":\"a4c40988-b3c5-486a-80f8-5eaf335e4d76\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1554"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.palettes import GnBu3, OrRd3\n",
    "\n",
    "years = ['2015', '2016', '2017']\n",
    "\n",
    "exports = {'fruits' : fruits,\n",
    "           '2015'   : [2, 1, 4, 3, 2, 4],\n",
    "           '2016'   : [5, 3, 4, 2, 4, 6],\n",
    "           '2017'   : [3, 2, 4, 4, 5, 3]}\n",
    "imports = {'fruits' : fruits,\n",
    "           '2015'   : [-1, 0, -1, -3, -2, -1],\n",
    "           '2016'   : [-2, -1, -3, -1, -2, -2],\n",
    "           '2017'   : [-1, -2, -1, 0, -2, -2]}\n",
    "\n",
    "p = figure(y_range=fruits, plot_height=250, x_range=(-16, 16), title=\"Fruit import/export, by year\")\n",
    "\n",
    "p.hbar_stack(years, y='fruits', height=0.9, color=GnBu3, source=ColumnDataSource(exports),\n",
    "             legend_label=[\"%s exports\" % x for x in years])\n",
    "\n",
    "p.hbar_stack(years, y='fruits', height=0.9, color=OrRd3, source=ColumnDataSource(imports),\n",
    "             legend_label=[\"%s imports\" % x for x in years])\n",
    "\n",
    "p.y_range.range_padding = 0.1\n",
    "p.ygrid.grid_line_color = None\n",
    "p.legend.location = \"center_left\"\n",
    "\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice we also added some padding *around* the categorical range (e.g. at both ends of the axis) by specifying\n",
    "\n",
    "```\n",
    "p.y_range.range_padding = 0.1\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a stacked bar chart with a single call to vbar_stack\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grouped Bar Charts\n",
    "\n",
    "Sometimes we want to group bars together, instead of stacking them. Bokeh can handle up to three levels of nested (hierarchical) categories, and will automatically group output according to the outermost level. To specify neted categorical coordinates, the columns of the data source should contain tuples, for example:\n",
    "\n",
    "    x = [ (\"Apples\", \"2015\"), (\"Apples\", \"2016\"), (\"Apples\", \"2017\"), (\"Pears\", \"2015), ... ]\n",
    "    \n",
    "Values in other columns correspond to each item in `x`, exactly as in other cases. When plotting with these kinds of nested coordinates, we must tell Bokeh the contents and order the axis range, by explicitly passing a `FactorRange` to `figure`. In the example below, this is seen as\n",
    "\n",
    "    p = figure(x_range=FactorRange(*x), ....)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"0d4965ed-30f0-4ab4-840c-56c1bdcdd380\" data-root-id=\"1794\"></div>\n"
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       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = 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   "source": [
    "from bokeh.models import FactorRange\n",
    "\n",
    "fruits = ['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries']\n",
    "years = ['2015', '2016', '2017']\n",
    "\n",
    "data = {'fruits' : fruits,\n",
    "        '2015'   : [2, 1, 4, 3, 2, 4],\n",
    "        '2016'   : [5, 3, 3, 2, 4, 6],\n",
    "        '2017'   : [3, 2, 4, 4, 5, 3]}\n",
    "\n",
    "# this creates [ (\"Apples\", \"2015\"), (\"Apples\", \"2016\"), (\"Apples\", \"2017\"), (\"Pears\", \"2015), ... ]\n",
    "x = [ (fruit, year) for fruit in fruits for year in years ]\n",
    "counts = sum(zip(data['2015'], data['2016'], data['2017']), ()) # like an hstack\n",
    "\n",
    "source = ColumnDataSource(data=dict(x=x, counts=counts))\n",
    "\n",
    "p = figure(x_range=FactorRange(*x), plot_height=250, title=\"Fruit Counts by Year\")\n",
    "\n",
    "p.vbar(x='x', top='counts', width=0.9, source=source)\n",
    "\n",
    "p.y_range.start = 0\n",
    "p.x_range.range_padding = 0.1\n",
    "p.xaxis.major_label_orientation = 1\n",
    "p.xgrid.grid_line_color = None\n",
    "\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exercise: Make the chart above have a different color for each year by adding colors to the ColumnDataSource\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another way we can set the color of the bars is to use a transorm. We first saw some transforms in previous chapter [Data Sources and Transformations](04 - Data Sources and Transformations.ipynb). Here we use a new one `factor_cmap` that accepts a the name of a column to use for colormapping, as well as the palette and factors that define the color mapping. \n",
    "\n",
    "Additionally we can configure it to map just the sub-factors if desired. For instance in this case we don't want shade each `(fruit, year)` pair differently. Instead, we want to only shade based on the `year`. So we pass `start=1` and `end=2` to specify the slice range of each factor to use when colormapping. Then we pass the result as the `fill_color` value:\n",
    "\n",
    "```\n",
    "    fill_color=factor_cmap('x', palette=['firebrick', 'olive', 'navy'], factors=years, start=1, end=2))\n",
    "```\n",
    "to have the colors be applied automatically based on the underlying data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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       "id": "1932"
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   "source": [
    "from bokeh.transform import factor_cmap\n",
    "\n",
    "p = figure(x_range=FactorRange(*x), plot_height=250, title=\"Fruit Counts by Year\")\n",
    "\n",
    "p.vbar(x='x', top='counts', width=0.9, source=source, line_color=\"white\",\n",
    "\n",
    "       # use the palette to colormap based on the the x[1:2] values\n",
    "       fill_color=factor_cmap('x', palette=['firebrick', 'olive', 'navy'], factors=years, start=1, end=2))\n",
    "\n",
    "p.y_range.start = 0\n",
    "p.x_range.range_padding = 0.1\n",
    "p.xaxis.major_label_orientation = 1\n",
    "p.xgrid.grid_line_color = None\n",
    "\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is also possible to achieve grouped bar plots using another technique called \"visual dodge\". That would be useful e.g. if you only wanted to have the axis labeled by fruit type, and not include the years on the axis. This tutorial does not cover that technique but you can find information in the [User's Guide](http://bokeh.pydata.org/en/dev/docs/user_guide/categorical.html#visual-dodge). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mixing Categorical Levels\n",
    "\n",
    "If you have created a range with nested categories as above, it is possible to plot glyphs using only the \"outer\" categories, if desired. The plot below shows monthly values grouped by quarter as bars. The data for these are in the famliar format:\n",
    "\n",
    "    factors = [(\"Q1\", \"jan\"), (\"Q1\", \"feb\"), (\"Q1\", \"mar\"), ....]\n",
    "\n",
    "The plot also overlays a line representing average quarterly values, and this is accomplished by using only the \"quarter\" part of each nexted category:\n",
    "\n",
    "    p.line(x=[\"Q1\", \"Q2\", \"Q3\", \"Q4\"], y=....)"
   ]
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  {
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   "execution_count": 14,
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Application\",\"version\":\"1.4.0\"}};\n",
       "  var render_items = [{\"docid\":\"ac26a874-dc7b-479c-af4f-0a5d0cbeace9\",\"roots\":{\"2079\":\"1569ad9b-6f0e-44f0-8ebd-b978df1790b8\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
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       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
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       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "2079"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "factors = [(\"Q1\", \"jan\"), (\"Q1\", \"feb\"), (\"Q1\", \"mar\"),\n",
    "           (\"Q2\", \"apr\"), (\"Q2\", \"may\"), (\"Q2\", \"jun\"),\n",
    "           (\"Q3\", \"jul\"), (\"Q3\", \"aug\"), (\"Q3\", \"sep\"),\n",
    "           (\"Q4\", \"oct\"), (\"Q4\", \"nov\"), (\"Q4\", \"dec\")]\n",
    "\n",
    "p = figure(x_range=FactorRange(*factors), plot_height=250)\n",
    "\n",
    "x = [ 10, 12, 16, 9, 10, 8, 12, 13, 14, 14, 12, 16 ]\n",
    "p.vbar(x=factors, top=x, width=0.9, alpha=0.5)\n",
    "\n",
    "qs, aves = [\"Q1\", \"Q2\", \"Q3\", \"Q4\"], [12, 9, 13, 14]\n",
    "p.line(x=qs, y=aves, color=\"red\", line_width=3)\n",
    "p.circle(x=qs, y=aves, line_color=\"red\", fill_color=\"white\", size=10)\n",
    "\n",
    "p.y_range.start = 0\n",
    "p.x_range.range_padding = 0.1\n",
    "p.xgrid.grid_line_color = None\n",
    "\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Pandas `GroupBy`\n",
    "\n",
    "We may want to make charts based on the results of \"group by\" operations. Bokeh can utilize Pandas `GroupBy` objects directly to make this simpler. Let's take a look at how Bokeh deals with `GroupBy` objects by examining the \"cars\" data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cyl</th>\n",
       "      <th>displ</th>\n",
       "      <th>hp</th>\n",
       "      <th>weight</th>\n",
       "      <th>accel</th>\n",
       "      <th>yr</th>\n",
       "      <th>origin</th>\n",
       "      <th>name</th>\n",
       "      <th>mfr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130</td>\n",
       "      <td>3504</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>North America</td>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
       "      <td>chevrolet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165</td>\n",
       "      <td>3693</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70</td>\n",
       "      <td>North America</td>\n",
       "      <td>buick skylark 320</td>\n",
       "      <td>buick</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150</td>\n",
       "      <td>3436</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>North America</td>\n",
       "      <td>plymouth satellite</td>\n",
       "      <td>plymouth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150</td>\n",
       "      <td>3433</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>North America</td>\n",
       "      <td>amc rebel sst</td>\n",
       "      <td>amc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140</td>\n",
       "      <td>3449</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>North America</td>\n",
       "      <td>ford torino</td>\n",
       "      <td>ford</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mpg cyl  displ   hp  weight  accel  yr         origin  \\\n",
       "0  18.0   8  307.0  130    3504   12.0  70  North America   \n",
       "1  15.0   8  350.0  165    3693   11.5  70  North America   \n",
       "2  18.0   8  318.0  150    3436   11.0  70  North America   \n",
       "3  16.0   8  304.0  150    3433   12.0  70  North America   \n",
       "4  17.0   8  302.0  140    3449   10.5  70  North America   \n",
       "\n",
       "                        name        mfr  \n",
       "0  chevrolet chevelle malibu  chevrolet  \n",
       "1          buick skylark 320      buick  \n",
       "2         plymouth satellite   plymouth  \n",
       "3              amc rebel sst        amc  \n",
       "4                ford torino       ford  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from bokeh.sampledata.autompg import autompg_clean as df\n",
    "\n",
    "df.cyl = df.cyl.astype(str)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Suppose we would like to display some values grouped according to `\"cyl\"`. If we create `df.groupby(('cyl'))` then call `group.describe()` we can see that Pandas automatically computes various statistics for each group. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">mpg</th>\n",
       "      <th colspan=\"2\" halign=\"left\">displ</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">accel</th>\n",
       "      <th colspan=\"8\" halign=\"left\">yr</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
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       "    <tr>\n",
       "      <th>cyl</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>20.550000</td>\n",
       "      <td>2.564501</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.75</td>\n",
       "      <td>20.25</td>\n",
       "      <td>22.05</td>\n",
       "      <td>23.7</td>\n",
       "      <td>4.0</td>\n",
       "      <td>72.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>13.5</td>\n",
       "      <td>13.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>75.500000</td>\n",
       "      <td>3.696846</td>\n",
       "      <td>72.0</td>\n",
       "      <td>72.75</td>\n",
       "      <td>75.0</td>\n",
       "      <td>77.75</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>199.0</td>\n",
       "      <td>29.283920</td>\n",
       "      <td>5.670546</td>\n",
       "      <td>18.0</td>\n",
       "      <td>25.00</td>\n",
       "      <td>28.40</td>\n",
       "      <td>32.95</td>\n",
       "      <td>46.6</td>\n",
       "      <td>199.0</td>\n",
       "      <td>109.670854</td>\n",
       "      <td>...</td>\n",
       "      <td>18.0</td>\n",
       "      <td>24.8</td>\n",
       "      <td>199.0</td>\n",
       "      <td>77.030151</td>\n",
       "      <td>3.737484</td>\n",
       "      <td>70.0</td>\n",
       "      <td>74.00</td>\n",
       "      <td>77.0</td>\n",
       "      <td>80.00</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.0</td>\n",
       "      <td>27.366667</td>\n",
       "      <td>8.228204</td>\n",
       "      <td>20.3</td>\n",
       "      <td>22.85</td>\n",
       "      <td>25.40</td>\n",
       "      <td>30.90</td>\n",
       "      <td>36.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>78.0</td>\n",
       "      <td>78.50</td>\n",
       "      <td>79.0</td>\n",
       "      <td>79.50</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>83.0</td>\n",
       "      <td>19.973494</td>\n",
       "      <td>3.828809</td>\n",
       "      <td>15.0</td>\n",
       "      <td>18.00</td>\n",
       "      <td>19.00</td>\n",
       "      <td>21.00</td>\n",
       "      <td>38.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>218.361446</td>\n",
       "      <td>...</td>\n",
       "      <td>17.6</td>\n",
       "      <td>21.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>75.951807</td>\n",
       "      <td>3.264381</td>\n",
       "      <td>70.0</td>\n",
       "      <td>74.00</td>\n",
       "      <td>76.0</td>\n",
       "      <td>78.00</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>103.0</td>\n",
       "      <td>14.963107</td>\n",
       "      <td>2.836284</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>26.6</td>\n",
       "      <td>103.0</td>\n",
       "      <td>345.009709</td>\n",
       "      <td>...</td>\n",
       "      <td>14.0</td>\n",
       "      <td>22.2</td>\n",
       "      <td>103.0</td>\n",
       "      <td>73.902913</td>\n",
       "      <td>3.021214</td>\n",
       "      <td>70.0</td>\n",
       "      <td>72.00</td>\n",
       "      <td>73.0</td>\n",
       "      <td>76.00</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       mpg                                                        displ  \\\n",
       "     count       mean       std   min    25%    50%    75%   max  count   \n",
       "cyl                                                                       \n",
       "3      4.0  20.550000  2.564501  18.0  18.75  20.25  22.05  23.7    4.0   \n",
       "4    199.0  29.283920  5.670546  18.0  25.00  28.40  32.95  46.6  199.0   \n",
       "5      3.0  27.366667  8.228204  20.3  22.85  25.40  30.90  36.4    3.0   \n",
       "6     83.0  19.973494  3.828809  15.0  18.00  19.00  21.00  38.0   83.0   \n",
       "8    103.0  14.963107  2.836284   9.0  13.00  14.00  16.00  26.6  103.0   \n",
       "\n",
       "                 ... accel           yr                                    \\\n",
       "           mean  ...   75%   max  count       mean       std   min    25%   \n",
       "cyl              ...                                                        \n",
       "3     72.500000  ...  13.5  13.5    4.0  75.500000  3.696846  72.0  72.75   \n",
       "4    109.670854  ...  18.0  24.8  199.0  77.030151  3.737484  70.0  74.00   \n",
       "5    145.000000  ...  20.0  20.1    3.0  79.000000  1.000000  78.0  78.50   \n",
       "6    218.361446  ...  17.6  21.0   83.0  75.951807  3.264381  70.0  74.00   \n",
       "8    345.009709  ...  14.0  22.2  103.0  73.902913  3.021214  70.0  72.00   \n",
       "\n",
       "                        \n",
       "      50%    75%   max  \n",
       "cyl                     \n",
       "3    75.0  77.75  80.0  \n",
       "4    77.0  80.00  82.0  \n",
       "5    79.0  79.50  80.0  \n",
       "6    76.0  78.00  82.0  \n",
       "8    73.0  76.00  81.0  \n",
       "\n",
       "[5 rows x 48 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group = df.groupby(('cyl'))\n",
    "\n",
    "group.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bokeh allows us to create a `ColumnDataSource` directly from Pandas `GroupBy` objects, and when this happens, the data source is automatically filled with the summary values from `group.desribe()`. Observe the column names below, which correspond to the output above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cyl,mpg_count,mpg_mean,mpg_std,mpg_min,mpg_25%,mpg_50%,mpg_75%,mpg_max,displ_count,displ_mean,displ_std,displ_min,displ_25%,displ_50%,displ_75%,displ_max,hp_count,hp_mean,hp_std,hp_min,hp_25%,hp_50%,hp_75%,hp_max,weight_count,weight_mean,weight_std,weight_min,weight_25%,weight_50%,weight_75%,weight_max,accel_count,accel_mean,accel_std,accel_min,accel_25%,accel_50%,accel_75%,accel_max,yr_count,yr_mean,yr_std,yr_min,yr_25%,yr_50%,yr_75%,yr_max'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "source = ColumnDataSource(group)\n",
    "\n",
    "\",\".join(source.column_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Knowing these column names, we can immediately create bar charts based on Pandas `GroupBy` objects. The example below plots the aveage MPG per cylinder, i.e. columns `\"mpg_mean\"` vs `\"cyl\"`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"24b71406-ab47-46d4-961f-49df5f35a859\" data-root-id=\"2277\"></div>\n"
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     "output_type": "display_data"
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       "  function embed_document(root) {\n",
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Application\",\"version\":\"1.4.0\"}};\n",
       "  var render_items = [{\"docid\":\"951a7831-6876-475a-980e-ba219916d09b\",\"roots\":{\"2277\":\"24b71406-ab47-46d4-961f-49df5f35a859\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "2277"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.palettes import Spectral5\n",
    "\n",
    "cyl_cmap = factor_cmap('cyl', palette=Spectral5, factors=sorted(df.cyl.unique()))\n",
    "\n",
    "p = figure(plot_height=350, x_range=group)\n",
    "p.vbar(x='cyl', top='mpg_mean', width=1, line_color=\"white\", \n",
    "       fill_color=cyl_cmap, source=source)\n",
    "\n",
    "p.xgrid.grid_line_color = None\n",
    "p.xaxis.axis_label = \"number of cylinders\"\n",
    "p.yaxis.axis_label = \"Mean MPG\"\n",
    "p.y_range.start = 0\n",
    "\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exercise: Use the same dataset to make a similar plot of mean horsepower (hp) by origin\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Catgorical Scatterplots\n",
    "\n",
    "So far we have seen Categorical data used together with various bar glyphs. But Bokeh can use categorical coordinates for most any glyphs. Let's create a scatter plot with categorical coordinates on one axis. The `commits` data set simply has a series datetimes of GitHub commit. Additional columns to express the day and hour of day for each commit have already been added."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-04-22 15:11:58-05:00</th>\n",
       "      <td>Sat</td>\n",
       "      <td>15:11:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-21 14:20:57-05:00</th>\n",
       "      <td>Fri</td>\n",
       "      <td>14:20:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-20 14:35:08-05:00</th>\n",
       "      <td>Thu</td>\n",
       "      <td>14:35:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-20 10:34:29-05:00</th>\n",
       "      <td>Thu</td>\n",
       "      <td>10:34:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-20 09:17:23-05:00</th>\n",
       "      <td>Thu</td>\n",
       "      <td>09:17:23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           day      time\n",
       "datetime                                \n",
       "2017-04-22 15:11:58-05:00  Sat  15:11:58\n",
       "2017-04-21 14:20:57-05:00  Fri  14:20:57\n",
       "2017-04-20 14:35:08-05:00  Thu  14:35:08\n",
       "2017-04-20 10:34:29-05:00  Thu  10:34:29\n",
       "2017-04-20 09:17:23-05:00  Thu  09:17:23"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from bokeh.sampledata.commits import data\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create our scatter plot, we pass the list of categories as the range just as before\n",
    "\n",
    "    p = figure(y_range=DAYS, ...)\n",
    "    \n",
    "Then we can plot circles for each commit, with `\"time\"` driving the x-coordinate, and `\"day\"` driving the y-coordinate.\n",
    "\n",
    "    p.circle(x='time', y='day', ...)\n",
    "\n",
    "To make the values more distinguishable, we can also add a `jitter` transform to the y-coordinate, which is shown in the complete example below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"fe19add1-1751-4b45-94f6-619a47deabd7\" data-root-id=\"2450\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = 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2012\\u20142016\"},\"id\":\"2451\",\"type\":\"Title\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"2470\",\"type\":\"PanTool\"},{\"id\":\"2471\",\"type\":\"WheelZoomTool\"},{\"id\":\"2472\",\"type\":\"BoxZoomTool\"},{\"id\":\"2473\",\"type\":\"SaveTool\"},{\"id\":\"2474\",\"type\":\"ResetTool\"},{\"id\":\"2475\",\"type\":\"HelpTool\"}]},\"id\":\"2476\",\"type\":\"Toolbar\"},{\"attributes\":{\"months\":[0,1,2,3,4,5,6,7,8,9,10,11]},\"id\":\"2585\",\"type\":\"MonthsTicker\"},{\"attributes\":{\"months\":[0,4,8]},\"id\":\"2587\",\"type\":\"MonthsTicker\"},{\"attributes\":{\"days\":[1,8,15,22]},\"id\":\"2583\",\"type\":\"DaysTicker\"},{\"attributes\":{\"mantissas\":[1,2,5],\"max_interval\":500.0,\"num_minor_ticks\":0},\"id\":\"2578\",\"type\":\"AdaptiveTicker\"},{\"attributes\":{},\"id\":\"2470\",\"type\":\"PanTool\"},{\"attributes\":{},\"id\":\"2590\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"days\":[1,4,7,10,13,16,19,22,25,28]},\"id\":\"2582\",\"type\":\"DaysTicker\"},{\"attributes\":{\"callback\":null,\"range_padding\":0},\"id\":\"2453\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"2471\",\"type\":\"WheelZoomTool\"},{\"attributes\":{},\"id\":\"2475\",\"type\":\"HelpTool\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"2577\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"months\":[0,6]},\"id\":\"2588\",\"type\":\"MonthsTicker\"},{\"attributes\":{\"overlay\":{\"id\":\"2577\",\"type\":\"BoxAnnotation\"}},\"id\":\"2472\",\"type\":\"BoxZoomTool\"},{\"attributes\":{},\"id\":\"2473\",\"type\":\"SaveTool\"},{\"attributes\":{\"base\":24,\"mantissas\":[1,2,4,6,8,12],\"max_interval\":43200000.0,\"min_interval\":3600000.0,\"num_minor_ticks\":0},\"id\":\"2580\",\"type\":\"AdaptiveTicker\"},{\"attributes\":{},\"id\":\"2459\",\"type\":\"CategoricalScale\"},{\"attributes\":{},\"id\":\"2457\",\"type\":\"LinearScale\"},{\"attributes\":{\"days\":[\"%Hh\"]},\"id\":\"2489\",\"type\":\"DatetimeTickFormatter\"},{\"attributes\":{\"callback\":null,\"factors\":[\"Sun\",\"Sat\",\"Fri\",\"Thu\",\"Wed\",\"Tue\",\"Mon\"]},\"id\":\"2455\",\"type\":\"FactorRange\"},{\"attributes\":{\"days\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31]},\"id\":\"2581\",\"type\":\"DaysTicker\"},{\"attributes\":{\"base\":60,\"mantissas\":[1,2,5,10,15,20,30],\"max_interval\":1800000.0,\"min_interval\":1000.0,\"num_minor_ticks\":0},\"id\":\"2579\",\"type\":\"AdaptiveTicker\"},{\"attributes\":{},\"id\":\"2591\",\"type\":\"Selection\"},{\"attributes\":{\"data_source\":{\"id\":\"2449\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"2485\",\"type\":\"Circle\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"2486\",\"type\":\"Circle\"},\"selection_glyph\":null,\"view\":{\"id\":\"2488\",\"type\":\"CDSView\"}},\"id\":\"2487\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"2589\",\"type\":\"YearsTicker\"},{\"attributes\":{},\"id\":\"2576\",\"type\":\"CategoricalTickFormatter\"}],\"root_ids\":[\"2450\"]},\"title\":\"Bokeh Application\",\"version\":\"1.4.0\"}};\n",
       "  var render_items = [{\"docid\":\"0a82e52b-bc30-4938-8a4e-18180da19635\",\"roots\":{\"2450\":\"fe19add1-1751-4b45-94f6-619a47deabd7\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "2450"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.transform import jitter\n",
    "\n",
    "DAYS = ['Sun', 'Sat', 'Fri', 'Thu', 'Wed', 'Tue', 'Mon']\n",
    "\n",
    "source = ColumnDataSource(data)\n",
    "\n",
    "p = figure(plot_width=800, plot_height=300, y_range=DAYS, x_axis_type='datetime', \n",
    "           title=\"Commits by Time of Day (US/Central) 2012—2016\")\n",
    "\n",
    "p.circle(x='time', y=jitter('day', width=0.6, range=p.y_range),  source=source, alpha=0.3)\n",
    "\n",
    "p.xaxis[0].formatter.days = ['%Hh']\n",
    "p.x_range.range_padding = 0\n",
    "p.ygrid.grid_line_color = None\n",
    "\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exercise: Create a plot using categorical coordinates and any non-\"bar\" glyphs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
